When batteries supply behind-the-meter services such as arbitrage or peak load management, an optimal controller can be designed to minimize the total electric bill. The limitations of the batteries, such as on voltage or state-of-charge, are represented in the model used to forecast the system's state dynamics. Control model inaccuracy can lead to an optimistic shortfall, where the achievable schedule will be costlier than the schedule derived using the model.

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Documentation: 
[1] David Rosewater, "Risk-Averse Model Predictive Control Design for Battery Energy Storage Systems", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/722e-jp30. Accessed: Dec. 14, 2024.
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doi = {10.21227/722e-jp30},
url = {http://dx.doi.org/10.21227/722e-jp30},
author = {David Rosewater },
publisher = {IEEE Dataport},
title = {Risk-Averse Model Predictive Control Design for Battery Energy Storage Systems},
year = {2019} }
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T1 - Risk-Averse Model Predictive Control Design for Battery Energy Storage Systems
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David Rosewater. (2019). Risk-Averse Model Predictive Control Design for Battery Energy Storage Systems. IEEE Dataport. http://dx.doi.org/10.21227/722e-jp30
David Rosewater, 2019. Risk-Averse Model Predictive Control Design for Battery Energy Storage Systems. Available at: http://dx.doi.org/10.21227/722e-jp30.
David Rosewater. (2019). "Risk-Averse Model Predictive Control Design for Battery Energy Storage Systems." Web.
1. David Rosewater. Risk-Averse Model Predictive Control Design for Battery Energy Storage Systems [Internet]. IEEE Dataport; 2019. Available from : http://dx.doi.org/10.21227/722e-jp30
David Rosewater. "Risk-Averse Model Predictive Control Design for Battery Energy Storage Systems." doi: 10.21227/722e-jp30